Search Results (124)

Acoustic behavior, essential for communication and perception, is metabolically demanding. Studying the energy costs of echolocation helps us to understand animal energy allocation and provides key insights into the evolutionary constraints of acoustic signals. We examined the constant-frequency bat Rhinolophus nippon using a miniature electrocardiogram system and a custom servomotor that moved prey toward stationary bats. This setup allowed for synchronous recording of high-resolution electrocardiogram and echolocation calls from the search phase to the approach phase. During the search phase, bats emitted isolated echolocation pulses characterized by long pulse durations and inter-pulse intervals (IPIs), together with higher root mean square (RMS) amplitude, pulse energy, and peak amplitude. In the approach phase, call rate increased significantly (3.15-fold), and bats predominantly produced sonar sound groups. Meanwhile, pulse duration, IPIs, RMS amplitude, and pulse energy decreased to 65.23%, 25.82%, 78.50%, and 86.32% of the corresponding search-phase values, whereas peak amplitude increased to 110.99%, indicating that R. nippon can flexibly adjust the structure of its echolocation calls. However, despite the increased call rate (p < 0.05), neither heart rate nor metabolic rate differed between phases. This study provides direct physiological evidence for understanding energy expenditure in bat echolocation and offers a methodological reference for future research. Full article

Cardiac auscultation remains a widely used non-invasive method for assessing cardiac function; however, conventional acoustic stethoscopes are limited by subjective interpretation and lack of digital signal-handling capabilities. This study presents the design and fabrication of a chitosan-based diaphragm digital stethoscope using a biopolymer-derived acoustic interface. Chitosan was extracted from mealworm larvae shells through sequential chemical processing and subsequently processed into a glycerol-plasticized film via solution casting to obtain a flexible diaphragm. The mechanical properties of the diaphragm were evaluated to assess its suitability for acoustic applications. The diaphragm was mechanically coupled to a piezoelectric sensor and integrated into a custom 3D-printed chest piece connected to a microcontroller-based acquisition system. Heart sound signals were acquired from four conventional auscultation sites (aortic, pulmonic, tricuspid, and mitral regions). The recorded signals were processed using band-pass filtering, envelope extraction, and time–frequency analysis to visualize waveform morphology and frequency content. The signals obtained exhibited temporal and spectral features consistent with reported phonocardiography characteristics, including identifiable S1 and S2 components. These results demonstrate the feasibility of using chitosan-based diaphragm materials for heart sound acquisition in a digital stethoscope configuration, providing a low-complexity platform for further development of biopolymer-based acoustic sensing devices. Full article

Background/Objectives: Cardiovascular diseases are the leading cause of death worldwide. Therefore, early diagnosis and treatment of these diseases are of critical importance. Stethoscopes are the easiest and fastest medical devices for the initial diagnosis of cardiovascular diseases. However, interpreting heart sounds requires considerable expertise. The use of artificial intelligence in healthcare for decision support has increased and become popular recently. Methods: The popular 2016 PhysioNet/CinC Challenge dataset, consisting of phonocardiogram (PCG) signals, was used to implement the proposed approach. Spectrogram and continuous wavelet transform (CWT) images of the PCG signals were first generated. This increased the distinguishability of the data in terms of both time and frequency components. These two-input images were tested on the developed Dual Time–Frequency Swin Transformer–ConvNeXt Attention Network (DTF-STCANet) model. To further improve classification accuracy, the Weighted KNN algorithm was preferred during the classification phase. Results: With the proposed approach, a 99.29% classification accuracy was achieved. Performance was compared with other state-of-the-art models. Conclusions: The proposed approach, through the integration of PCG signals with artificial intelligence, further strengthens the concept of early diagnosis of heart disease. Full article

Phonocardiogram (PCG) signals offer a non-invasive, low-cost screening tool for cardiovascular diseases. However, their noisy and non-stationary nature makes automated classification challenging, and traditional methods often fail to capture complex spectral-temporal patterns. This study proposes a multimodal deep learning architecture for the binary classification of heart sounds (Healthy vs. Unhealthy). The hybrid model integrates Convolutional Neural Networks (CNNs), Transformer encoders, and Bidirectional Long Short-Term Memory (BiLSTM) networks with an attention mechanism. It utilizes an early-fusion feature extraction pipeline combining MFCCs, Mel-spectrograms, and Chroma descriptors. To ensure robust evaluation and prevent data leakage, SMOTE is applied exclusively to the training folds within a strict zero-leakage, patient-wise 5-fold cross-validation protocol. The proposed framework demonstrates exceptional performance, achieving an average accuracy of 91.67%, a sensitivity of 80.95%, a specificity of 94.46%, and an AUC-ROC of 96.50%. An ablation study confirms that integrating Transformer and BiLSTM modules significantly enhances diagnostic stability over baseline CNNs. Furthermore, with exactly 858,434 parameters (3.27 MB) and interpretable attention maps, this highly optimized model provides a robust assistive solution suitable for deployment in digital stethoscopes and mobile telemedicine systems. Full article

Heart and respiratory rate monitoring during sleep enables the detection of physiological irregularities through contact or contactless methods. Traditional approaches like polysomnography are accurate but costly, ergonomically limited, and often poorly accepted by patients. Smart Bedding^® is a novel, flexible bedsheet equipped with a high-resolution sensor network that records movement, pressure, sound, temperature, and humidity throughout the night. This study aimed to estimate cardiorespiratory parameters using the Smart Bedding^® IMU. Data from 30 participants sleeping on Smart Bedding^® while undergoing simultaneous polysomnography were analyzed. A robust and low-cost preprocessing pipeline was developed; estimation was performed using zero-crossing, peak detection, and Burg’s method for comparison, and validation was conducted using polysomnography as the gold-standard reference. Respiratory and heart rates were accurately estimated, achieving overall accuracies of 93.9% and 88.7% using zero-crossing and peak detection, respectively. Respiratory rate estimation showed no significant limitations across the frequency spectrum or among sleeping positions. However, heart rate estimation accuracy decreased when the frequency was below 55 BPM or when participants slept in a lateral sleep position, likely due to reduced cardiac signal power. Overall, the proposed methodology accurately tracked respiratory and cardiac patterns throughout the night, supporting Smart Bedding^® as a promising tool for future sleep tracking applications. Full article

Early and reliable detection of cardiac disease is crucial for preventing complications and enhancing patient outcomes. Phonocardiogram (PCG) signals, which encode rich information about cardiac function, offer a non-invasive and cost-effective way to identify abnormalities such as valvular disorders, arrhythmias, and other heart pathologies. This study investigates advanced diagnostic methods for heart sound analysis to improve the detection and classification of cardiac abnormalities. In the proposed framework, recurrence plots (RPs) are used for feature extraction, while machine learning algorithms are applied for classification, creating a diagnostic model that can recognize cardiac conditions from composite acoustic signals. This method serves as an efficient alternative to more computationally intensive deep learning methods and other high-dimensional ML-based solutions. Experimental results demonstrate that the multiclass classification task achieves up to 98.4% accuracy, and the binary classification reaches 99.5% accuracy using 2 s signal segments. The techniques assessed in this research demonstrate the potential of automated heart sound analysis as a screening tool in both clinical and remote healthcare settings. Overall, the findings highlight the significance of machine learning in heart sound classification and its potential to facilitate timely, accessible, and cost-effective cardiovascular care. Full article

Background/Objectives: Cardiovascular diseases are the leading cause of mortality worldwide according to the World Health Organization (WHO), highlighting the need for accessible tools for early detection. Automated classification systems based on signal processing and machine learning offer a non-invasive alternative to support clinical diagnosis. Methods: This study implements and evaluates machine learning models for distinguishing normal and abnormal heart sounds using a hybrid feature extraction approach. Recordings labeled as normal, murmur, and extrasystolic were obtained from the PASCAL dataset and subsequently binarized into two classes. Multiple numerical datasets were generated through statistical features derived from Mel-Frequency Cepstral Coefficients (MFCCs) and Daubechies wavelet analysis. Each dataset was standardized and used to train four classifiers: support vector machines, logistic regression, random forests, and decision trees. Results: Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and area under curve. All classifiers achieved notable results; however, the support vector machine model trained with 26 MFCCs and Daubechies-4 wavelet coefficients obtained the best performance. Conclusions: These findings demonstrate that the proposed hybrid MFCC–Wavelet framework provides competitive diagnostic accuracy and represents a lightweight, interpretable, and computationally efficient solution for computer-aided auscultation and early cardiovascular screening. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

Heart sounds contain critical information about valve activity and hemodynamics, serving as an essential basis for cardiovascular disease diagnosis. However, traditional heart sound sensors are either rigid or flexible but non-stretchable, limiting their ability to accommodate chest deformation and leading to signal distortion. This study proposes an easy-to-fabricate, stretchable piezoelectric heart sound sensor with a Kirigami-inspired design, a five-layer “sandwich” structure. Periodic Kirigami cuts significantly enhance stretchability while maintaining piezoelectric conversion efficiency. Finite element simulations reveal the Kirigami structure is more sensitive to hinge length and thickness than to hinge width. Electrical tests demonstrate a linear response to sound pressure, with output voltage rising from 0.11 V to 0.42 V (70–94 dB), and voltage amplitude increasing from 9 mV to 0.35 V (60–160 Hz). The sensor exhibits excellent stability, with a maximum amplitude variation of approximately 11% under 0–30% strain, a 17% voltage decrease at 11 mm bending radius, and less than 9% output fluctuation during 1200 s continuous excitation. Seven-day monitoring confirms reliable detection of the first (S1) and second (S2) heart sounds, with signals highly consistent with ECG and a commercial sensor, verifying its potential for wearable long-term monitoring and early cardiovascular disease screening. Full article

Blood pressure (BP) monitoring is essential for cardiovascular health management, yet traditional cuff-based methods face limitations including patient discomfort and inapplicability for certain populations. This study presents a deep learning framework for cuffless BP estimation using phonocardiogram (PCG) signals. The proposed model integrates convolutional neural networks (CNNs) with Squeeze-and-Excitation (SE) blocks and demographic information to enhance prediction accuracy. Mel-Frequency Cepstral Coefficients (MFCCs), along with their delta and delta–delta coefficients, were employed to capture comprehensive acoustic characteristics of heart sounds. The results demonstrated that the proposed model achieved high predictive accuracy and strong consistency with reference BP measurements. Component analysis confirmed that the inclusion of SE blocks provided substantial performance gains, while demographic information further improved prediction stability. Clinical validation also verified that the model maintained close agreement with true BP values across the tested population, showing significant improvement over the baseline CNN implementation. These findings suggest potential for accessible, non-invasive BP monitoring systems suitable for continuous health tracking. Full article

Cardiovascular diseases pose a major global health threat, making early automated detection through heart sound analysis crucial for their prevention. However, existing deep learning-based heart sound detection methods have shortcomings in feature extraction, and current attention mechanisms perform inadequately in capturing key heart sound features. To address this, we first introduce a Multi-Scale Feature Extraction Network composed of Multi-Scale Inverted Residual (MIR) modules and Dynamically Gated Convolution (DGC) modules to extract heart sound features effectively. The MIR module can efficiently extract multi-scale heart sound features, and the DGC module enhances the network’s representation ability by capturing feature interrelationships and dynamically adjusting information flow. Subsequently, a Multi-Scale Attention Prediction Network is designed for heart sound feature classification, which includes a multi-scale attention (MSA) module. The MSA module effectively captures subtle pathological features of heart sound signals through multi-scale feature extraction and cross-scale feature interaction. Additionally, pitch-shifting techniques are applied in the preprocessing stage to enhance the model’s generalization ability, and multiple feature extraction techniques are used for initial feature extraction of heart sounds. Evaluated via five-fold cross-validation, the model achieved accuracies of 98.89% and 98.86% on the PhysioNet/CinC 2016 and 2022 datasets, respectively, demonstrating superior performance and strong potential for clinical application. Full article

Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning. Full article

Background/Objectives: Cardiovascular diseases are among the leading causes of death worldwide. Early diagnosis of these conditions minimizes the risk of future death. Listening to heart sounds with a stethoscope is one of the easiest and fastest methods for diagnosing heart conditions. While heart sounds are a quick and easy diagnostic method, they require significant expert interpretation. Recently, artificial intelligence models trained based on these expert interpretations have become popular in the development of decision support systems. Methods: The proposed approach uses the popular 2016 PhysioNet/CinC Challenge dataset for PCG signals. Spectrogram image transformation was then performed to increase the representativeness of these signals. A deep learning-based model that allows for the simultaneous training of residual and attention blocks and the MLP-mixer model was used for feature extraction. A new algorithm combining the strengths of NCA and ReliefF algorithms was proposed to select the strongest features in the feature set. The SVM algorithm was used for classification. Results: With this proposed approach, over 98% success was achieved in all accuracy, sensitivity, specificity, precision, and F1-score metrics. Conclusions: As a result, an artificial intelligence-based decision support system that detects cardiovascular diseases with high accuracy is presented. Full article

Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart’s electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient for identifying certain conditions, such as valvular disorders. Phonocardiography (PCG) allows the recording and analysis of heart sounds and improves the diagnostic accuracy when combined with ECG. In this study, ECG and PCG signals were simultaneously acquired from a resting adult subject using a compact system comprising an analog front-end (model AD8232, manufactured by Analog Devices, Wilmington, MA, USA) for ECG acquisition and a digital stethoscope built around a condenser electret microphone (model HM-9250, manufactured by HMYL, Anqing, China). Both the ECG electrodes and the microphone were positioned on the chest to ensure the spatial alignment of the signals. An adaptive segmentation algorithm was developed to segment PCG and ECG signals based on their morphological and temporal features. This algorithm identifies the onset and peaks of S1 and S2 heart sounds in the PCG and the Q, R, and S waves in the ECG, enabling the extraction of the systolic time intervals such as EMAT, PEP, LVET, and LVST parameters proven useful in the diagnosis and monitoring of cardiovascular diseases. Based on the segmented signals, the measured averages (EMAT = 74.35 ms, PEP = 89.00 ms, LVET = 244.39 ms, LVST = 258.60 ms) were consistent with the reference standards, demonstrating the reliability of the developed method. The proposed algorithm was validated on synchronized ECG and PCG signals from multiple subjects in an open-source dataset (BSSLAB Localized ECG Data). The systolic intervals extracted using the proposed method closely matched the literature values, confirming the robustness across different recording conditions; in detail, the mean Q–S1 interval was 40.45 ms (≈45 ms reference value, mean difference: −4.85 ms, LoA: −3.42 ms and −6.09 ms) and the R–S1 interval was 14.09 ms (≈15 ms reference value, mean difference: −1.2 ms, LoA: −0.55 ms and −1.85 ms). In conclusion, the results demonstrate the potential of the joint ECG and PCG analysis to improve the long-term monitoring of cardiovascular diseases. Full article

The separation of respiratory and cardiac sounds is a significant challenge in biomedical signal processing due to their overlapping frequency and time characteristics. Traditional methods struggle with accurate extraction in noisy or diverse clinical environments. This study explores the application of machine learning, particularly convolutional neural networks (CNNs), to overcome these obstacles. Advanced machine learning models, denoising algorithms, and domain adaptation strategies address challenges such as frequency overlap, external noise, and limited labeled datasets. This study presents a robust methodology for detecting heart and lung diseases from audio signals using advanced preprocessing, feature extraction, and deep learning models. The approach integrates adaptive filtering and bandpass filtering as denoising techniques and variational autoencoders (VAEs) for feature extraction. The extracted features are input into a CNN, which classifies audio signals into different heart and lung conditions. The results highlight the potential of this combined approach for early and accurate disease detection, contributing to the development of reliable diagnostic tools for healthcare. Full article